Noud de Kroon has joined the UvA in October 2018 as a PhD student of AMLab, under the joint supervision of dr. Joris Mooij and dr. Danielle Belgrave (Microsoft Research Cambridge). Previously, he obtained a bachelor’s degree in software science at Eindhoven University of Technology and a master’s degree in computer science at the University of Oxford. His research focus is on combining causality and reinforcement learning in order to make better
decisions and improve data efficiency, with applications for example in the medical domain.
For more information on this vacancy, see Vacancies.
Tineke Blom joined AMLab as a PhD student on September 1st. Tineke studied Mathematical Sciences at Utrecht University and her research interests include causality, applied mathematics, and learning algorithms. She will conduct research on causality as part of the ERC starting grant project CAFES led by Joris Mooij.
An interdisciplinary team of AMLAB researchers, a biologist and a doctor won the first prize in the CRM Causal Inference Challenge (part of the Workshop Statistical Causal Inference and its Applications to Genetics, July 25 – August 19, Montreal, Canada). The team was led by Joris Mooij and consisted of AMLAB members Tom Claassen, Sara Magliacane, Philip Versteeg, Stephan Bongers, Thijs van Ommen, Patrick Forre, and external researchers Renée van Amerongen (Swammerdam Institute for Life Sciences) and Lucas van Eijk (Radboud University Medical Center). The task of the challenge was to predict values of certain phenotypic variables of knockout mice, given data from wildtype and other knockout mice.
Tom Claassen joined AMLab as a parttime postdoc (50%). Tom studied physics in Twente and worked for several years as a Systems Architect before doing his PhD on causal discovery and logic at the Radboud University Nijmegen. Tom will work on causality as a team member of the VIDI project of Joris Mooij.
You are all cordially invited to a presentation on Friday, April 8th, from 16:00-17:00 in C1.112 by Martin Gullaksen on his master’s thesis entitled “Probabilistic Spatio-Temporal Inference in Early Embryonic Development. The case of Drosophila Melanogaster“.
Abstract: Being able to infer gene regulatory networks from spatio-temporal expression
data is a major problem in biology. This thesis proposes a new dynamic Bayes
networks approach, which we benchmark by using the well researched gap gene
problem of the Drosophila melanogaster, with the capability of realistically
inferring gene regulatory networks and producing high quality simulations. The
thesis solves practical issues, currently associated with spatio-temporal gene
inference, such as computational time and parameter fragility, while obtaining
a similar gene regulatory network and matrix as our ground truth network. The
proposed modelling framework computes the gene regulatory network in 10-15
second on a modern laptop. Effectively removing the computational barrier of
the problem and allowing for future gene regulatory networks of greater gene
count to be processed. Besides producing a gene regulatory matrix our method
also produces high quality simulations of the gene activation levels of the gap
gene problem. In addition, unlike many competing problem formulations, the
proposed model is probabilistic in nature, hence allowing statistical inference
to be made. Finally, using Bayesian statistics, we perform robustness tests on
the topology of our proposed gene regulatory network and our regulatory